论文标题
选择建模的深度学习
Deep Learning for Choice Modeling
论文作者
论文摘要
选择建模一直是研究经济学，营销，运营研究和心理学在内的许多领域的个人偏好或实用性研究的核心主题。尽管有关选择模型的绝大多数文献已致力于导致管理和决策见解的分析属性，但从经验数据中学习选择模型的现有方法通常在计算上是棘手的，或者效率低下。在本文中，我们在两个选择模型的两个设置下开发了基于学习的选择模型：（i）无功能和（ii）基于功能。我们的模型既捕获了每个候选人选择的内在效用，又捕获了分类对选择概率的影响。合成和真实的数据实验证明了拟议模型的性能，从现有选择模型的恢复，样本复杂性，分类效果，体系结构设计和模型解释方面。
Choice modeling has been a central topic in the study of individual preference or utility across many fields including economics, marketing, operations research, and psychology. While the vast majority of the literature on choice models has been devoted to the analytical properties that lead to managerial and policy-making insights, the existing methods to learn a choice model from empirical data are often either computationally intractable or sample inefficient. In this paper, we develop deep learning-based choice models under two settings of choice modeling: (i) feature-free and (ii) feature-based. Our model captures both the intrinsic utility for each candidate choice and the effect that the assortment has on the choice probability. Synthetic and real data experiments demonstrate the performances of proposed models in terms of the recovery of the existing choice models, sample complexity, assortment effect, architecture design, and model interpretation.
